International Veterinary Epilepsy Task Force Consensus Proposal: Outcome of therapeutic interventions in canine and feline epilepsy

Common criteria for the diagnosis of drug resistance and the assessment of outcome are needed urgently as a prerequisite for standardized evaluation and reporting of individual therapeutic responses in canine epilepsy. Thus, we provide a proposal for the definition of drug resistance and partial therapeutic success in canine patients with epilepsy. This consensus statement also suggests a list of factors and aspects of outcome, which should be considered in addition to the impact on seizures. Moreover, these expert recommendations discuss criteria which determine the validity and informative value of a therapeutic trial in an individual patient and also suggest the application of individual outcome criteria. Agreement on common guidelines does not only render a basis for future optimization of individual patient management, but is also a presupposition for the design and implementation of clinical studies with highly standardized inclusion and exclusion criteria. Respective standardization will improve the comparability of findings from different studies and renders an improved basis for multicenter studies. Therefore, this proposal provides an in-depth discussion of the implications of outcome criteria for clinical studies. In particular ethical aspects and the different options for study design and application of individual patient-centered outcome criteria are considered

Time esophageal pH < 4 overestimates the prevalence of pathologic esophageal reflux in subjects with gastroesophageal reflux disease treated with proton pump inhibitors

<p>Abstract</p> <p>Background</p> <p>A Stanford University study reported that in asymptomatic GERD patients who were being treated with a proton pump inhibitor (PPI), 50% had pathologic esophageal acid exposure.</p> <p>Aim</p> <p>We considered the possibility that the high prevalence of pathologic esophageal reflux might simply have resulted from calculating acidity as time pH < 4.</p> <p>Methods</p> <p>We calculated integrated acidity and time pH < 4 from the 49 recordings of 24-hour gastric and esophageal pH from the Stanford study as well as from another study of 57 GERD subjects, 26 of whom were treated for 8 days with 20 mg omeprazole or 20 mg rabeprazole in a 2-way crossover fashion.</p> <p>Results</p> <p>The prevalence of pathologic 24-hour esophageal reflux in both studies was significantly higher when measured as time pH < 4 than when measured as integrated acidity. This difference was entirely attributable to a difference between the two measures during the nocturnal period. Nocturnal gastric acid breakthrough was not a useful predictor of pathologic nocturnal esophageal reflux.</p> <p>Conclusion</p> <p>In GERD subjects treated with a PPI, measuring time esophageal pH < 4 will significantly overestimate the prevalence of pathologic esophageal acid exposure over 24 hours and during the nocturnal period.</p

Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients. OBJECTIVES: We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR. METHODS: We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini's coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN. RESULTS: Forced LR solutions provided AUC 87.9+/-4.1% (CI: 80.7 to 93.2%) and 85.7+/-5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5+/-3.7% (CI: 83.8 to 95.1%). The Gini's coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini's coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN. CONCLUSIONS: Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly

Long-term mortality prediction after operations for type A ascending aortic dissection

<p>Abstract</p> <p>Background</p> <p>There are few long-term mortality prediction studies after acute aortic dissection (AAD) Type A and none were performed using new models such as neural networks (NN) or support vector machines (SVM) which may show a higher discriminatory potency than standard multivariable models.</p> <p>Methods</p> <p>We used 32 risk factors identified by Literature search and previously assessed in short-term outcome investigations. Models were trained (50%) and validated (50%) on 2 random samples from a consecutive 235-patient cohort. NN were run only on patients with complete data for all included variables (N = 211); SVM on the overall group. Discrimination was assessed by receiver operating characteristic area under the curve (AUC) and Gini's coefficients along with classification performance.</p> <p>Results</p> <p>There were 84 deaths (36%) occurring at 564 ± 48 days (95%CI from 470 to 658 days). Patients with complete variables had a slightly lower death rate (60 of 211, 28%). NN classified 44 of 60 (73%) dead patients and 147 of 151 (97%) long-term survivors using 5 covariates: immediate post-operative chronic renal failure, circulatory arrest time, the type of surgery on ascending aorta plus hemi-arch, extracorporeal circulation time and the presence of Marfan habitus. Global accuracies of training and validation NN were excellent with AUC respectively 0.871 and 0.870 but classification errors were high among patients who died. Training SVM, using a larger number of covariates, showed no false negative or false positive cases among 118 randomly selected patients (error = 0%, AUC 1.0) whereas validation SVM, among 117 patients, provided 5 false negative and 11 false positive cases (error = 22%, AUC 0.821, p < 0.01 versus NN results). An html file was produced to adopt and manipulate the selected parameters for practical predictive purposes.</p> <p>Conclusions</p> <p>Both NN and SVM accurately selected a few operative and immediate post-operative factors and the Marfan habitus as long-term mortality predictors in AAD Type A. Although these factors were not new per se, their combination may be used in practice to index death risk post-operatively with good accuracy.</p

Latent physiological factors of complex human diseases revealed by independent component analysis of clinarrays

<p>Abstract</p> <p>Background</p> <p>Diagnosis and treatment of patients in the clinical setting is often driven by known symptomatic factors that distinguish one particular condition from another. Treatment based on noticeable symptoms, however, is limited to the types of clinical biomarkers collected, and is prone to overlooking dysfunctions in physiological factors not easily evident to medical practitioners. We used a vector-based representation of patient clinical biomarkers, or clinarrays, to search for latent physiological factors that underlie human diseases directly from clinical laboratory data. Knowledge of these factors could be used to improve assessment of disease severity and help to refine strategies for diagnosis and monitoring disease progression.</p> <p>Results</p> <p>Applying Independent Component Analysis on clinarrays built from patient laboratory measurements revealed both known and novel concomitant physiological factors for asthma, types 1 and 2 diabetes, cystic fibrosis, and Duchenne muscular dystrophy. Serum sodium was found to be the most significant factor for both type 1 and type 2 diabetes, and was also significant in asthma. TSH3, a measure of thyroid function, and blood urea nitrogen, indicative of kidney function, were factors unique to type 1 diabetes respective to type 2 diabetes. Platelet count was significant across all the diseases analyzed.</p> <p>Conclusions</p> <p>The results demonstrate that large-scale analyses of clinical biomarkers using unsupervised methods can offer novel insights into the pathophysiological basis of human disease, and suggest novel clinical utility of established laboratory measurements.</p

Newly diagnosed incident dizziness of older patients: a follow-up study in primary care

<p>Abstract</p> <p>Background</p> <p>Dizziness is a common complaint of older patients in primary care, yet not much is known about the course of incident dizziness. The aim of the study was to follow-up symptoms, subjective impairments and needs of older patients (≥65) with incident dizziness and to determine predictors of chronic dizziness. Furthermore, we analysed general practitioners' (GPs') initial diagnoses, referrals and revised diagnoses after six months.</p> <p>Methods</p> <p>An observational study was performed in 21 primary care practices in Germany, including a four-week and six-month follow-up. A questionnaire comprising characteristic matters of dizziness and a series of validated instruments was completed by 66 participants during enrolment and follow-up (after 1 month and 6 months). After six months, chart reviews and face-to-face interviews were also performed with the GPs.</p> <p>Results</p> <p>Mean scores of dizziness handicap, depression and quality of life were not or only slightly affected, and did not deteriorate during follow-up; however, 24 patients (34.8%) showed a moderate or severe dizziness handicap, and 43 (62.3%) showed a certain disability in terms of quality of life at the time of enrolment. In multivariate analysis, n = 44 patients suffering from chronic dizziness (dependent variable, i.e. relapsing or persistent at six months) initially had a greater dizziness handicap (OR 1.42, 95%CI 1.05-1.47) than patients with transient dizziness. GPs referred 47.8% of the patients to specialists who detected two additional cases of benign paroxysmal positional vertigo (BPPV).</p> <p>Conclusions</p> <p>New-onset dizziness relapsed or persisted in a considerable number of patients within six months. This was difficult to predict due to the patients' heterogeneous complaints and characteristics. Symptom persistence does not seem to be associated with deterioration of the psychological status in older primary care patients. Management strategies should routinely consider BPPV as differential diagnosis.</p

Genome Sequence Analysis of Dengue Virus 1 Isolated in Key West, Florida

Dengue virus (DENV) is transmitted to humans through the bite of mosquitoes. In November 2010, a dengue outbreak was reported in Monroe County in southern Florida (FL), including greater than 20 confirmed human cases. The virus collected from the human cases was verified as DENV serotype 1 (DENV-1) and one isolate was provided for sequence analysis. RNA was extracted from the DENV-1 isolate and was used in reverse transcription polymerase chain reaction (RT-PCR) to amplify PCR fragments to sequence. Nucleic acid primers were designed to generate overlapping PCR fragments that covered the entire genome. The DENV-1 isolate found in Key West (KW), FL was sequenced for whole genome characterization. Sequence assembly, Genbank searches, and recombination analyses were performed to verify the identity of the genome sequences and to determine percent similarity to known DENV-1 sequences. We show that the KW DENV-1 strain is 99% identical to Nicaraguan and Mexican DENV-1 strains. Phylogenetic and recombination analyses suggest that the DENV-1 isolated in KW originated from Nicaragua (NI) and the KW strain may circulate in KW. Also, recombination analysis results detected recombination events in the KW strain compared to DENV-1 strains from Puerto Rico. We evaluate the relative growth of KW strain of DENV-1 compared to other dengue viruses to determine whether the underlying genetics of the strain is associated with a replicative advantage, an important consideration since local transmission of DENV may result because domestic tourism can spread DENVs

A computational model of excitation and contraction in uterine myocytes from the pregnant rat

Aberrant uterine myometrial activities in humans are major health issues. However, the cellular and tissue mechanism(s) that maintain the uterine myometrium at rest during gestation, and that initiate and maintain long-lasting uterine contractions during delivery are incompletely understood. In this study we construct a computational model for describing the electrical activity (simple and complex action potentials), intracellular calcium dynamics and mechanical contractions of isolated uterine myocytes from the pregnant rat. The model reproduces variant types of action potentials – from spikes with a smooth plateau, to spikes with an oscillatory plateau, to bursts of spikes – that are seen during late gestation under different physiological conditions. The effects of the hormones oestradiol (via reductions in calcium and potassium selective channel conductance), oxytocin (via an increase in intracellular calcium release) and the tocolytic nifedipine (via a block of L-type calcium channels currents) on action potentials and contractions are also reproduced, which quantitatively match to experimental data. All of these results validated the cell model development. In conclusion, the developed model provides a computational platform for further investigations of the ionic mechanism underlying the genesis and control of electrical and mechanical activities in the rat uterine myocytes

Pretransplant Prediction of Posttransplant Survival for Liver Recipients with Benign End-Stage Liver Diseases: A Nonlinear Model

Background: The scarcity of grafts available necessitates a system that considers expected posttransplant survival, in addition to pretransplant mortality as estimated by the MELD. So far, however, conventional linear techniques have failed to achieve sufficient accuracy in posttransplant outcome prediction. In this study, we aim to develop a pretransplant predictive model for liver recipients ’ survival with benign end-stage liver diseases (BESLD) by a nonlinear method based on pretransplant characteristics, and compare its performance with a BESLD-specific prognostic model (MELD) and a generalillness severity model (the sequential organ failure assessment score, or SOFA score). Methodology/Principal Findings: With retrospectively collected data on 360 recipients receiving deceased-donor transplantation for BESLD between February 1999 and August 2009 in the west China hospital of Sichuan university, we developed a multi-layer perceptron (MLP) network to predict one-year and two-year survival probability after transplantation. The performances of the MLP, SOFA, and MELD were assessed by measuring both calibration ability and discriminative power, with Hosmer-Lemeshow test and receiver operating characteristic analysis, respectively. By the forward stepwise selection, donor age and BMI; serum concentration of HB, Crea, ALB, TB, ALT, INR, Na +; presence of pretransplant diabetes; dialysis prior to transplantation, and microbiologically proven sepsis were identified to be the optimal input features. The MLP, employing 18 input neurons and 12 hidden neurons, yielded high predictive accuracy, wit